NNs require a large number of images to find the weights that lead to the loss function global minima, even if the model is already pretrained on another dataset. In our case, we started the adaptation of the RetinaNet model to the GDXray dataset using fine tuning from a pretrained model based on Microsoft COCO datasetSee https://cocodataset.org/#home, that contains 2.5 million labelled instances in 328k RGB images of 91 object types that would be easily recognizable by a 4 year old child [1]}.
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